This paper studies estimation and inference of heterogeneous peer effects featuring group fixed effects and slope heterogeneity under latent structure. We adapt the Classifier-Lasso algorithm to consistently discover latent structures and determine the number of clusters. To solve the incidental parameter problem in the binary choice model with social interactions, we propose a parametric bootstrap method to debias and establish its asymptotic validity. Monte Carlo simulations confirm strong finite sample performance of our methods. In an application to students' risky behaviors, the algorithm detects two latent clusters and finds that peer effects are significant within one of the clusters, demonstrating the practical applicability in uncovering heterogeneous social interactions.

翻译：本文研究了在潜在结构下具有组固定效应和斜率异质性的异质性同伴效应的估计与推断。我们采用Classifier-Lasso算法来一致地发现潜在结构并确定聚类数量。为了解决带有社交互动的二元选择模型中的伴随参数问题，我们提出了一种参数化自助法进行去偏，并建立了其渐近有效性。蒙特卡洛模拟证实了我们的方法具有优异的有限样本性能。在一个关于学生风险行为的应用中，该算法检测到两个潜在聚类，并发现同伴效应在其中一个聚类内是显著的，这证明了该方法在揭示异质性社交互动方面的实际适用性。